






'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class   __SCREAMING_SNAKE_CASE   (      unittest.TestCase					):




 @property
 def   __magic_name__						(   self						:				Tuple   )						->    List[str]:
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__  :					str						=UNetaDModel(
           block_out_channels=(32, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=3       ,   out_channels=3       ,   down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,   up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,   )
       return model




 def   __magic_name__						(   self						:				int   )						->    List[Any]:
       SCREAMING_SNAKE_CASE__  :					int						=self.dummy_uncond_unet
       SCREAMING_SNAKE_CASE__  :					Dict						=KarrasVeScheduler()

       SCREAMING_SNAKE_CASE__  :					int						=KarrasVePipeline(unet=UpperCamelCase__       ,   scheduler=UpperCamelCase__   )
       pipe.to(UpperCamelCase__   )
       pipe.set_progress_bar_config(disable=UpperCamelCase__   )

       SCREAMING_SNAKE_CASE__  :					str						=torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=pipe(num_inference_steps=2       ,   generator=UpperCamelCase__       ,   output_type='''numpy'''   ).images

       SCREAMING_SNAKE_CASE__  :					Optional[int]						=torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__  :					Tuple						=pipe(num_inference_steps=2       ,   generator=UpperCamelCase__       ,   output_type='''numpy'''       ,   return_dict=UpperCamelCase__   )[0]

       SCREAMING_SNAKE_CASE__  :					List[Any]						=image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2






@slow
@require_torch
class   __SCREAMING_SNAKE_CASE   (      unittest.TestCase					):




 def   __magic_name__						(   self						:				Optional[Any]   )						->    Any:
       SCREAMING_SNAKE_CASE__  :					List[str]						='''google/ncsnpp-celebahq-256'''
       SCREAMING_SNAKE_CASE__  :					List[Any]						=UNetaDModel.from_pretrained(UpperCamelCase__   )
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=KarrasVeScheduler()

       SCREAMING_SNAKE_CASE__  :					Any						=KarrasVePipeline(unet=UpperCamelCase__       ,   scheduler=UpperCamelCase__   )
       pipe.to(UpperCamelCase__   )
       pipe.set_progress_bar_config(disable=UpperCamelCase__   )

       SCREAMING_SNAKE_CASE__  :					str						=torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__  :					str						=pipe(num_inference_steps=20       ,   generator=UpperCamelCase__       ,   output_type='''numpy'''   ).images

       SCREAMING_SNAKE_CASE__  :					Dict						=image[0, -3:, -3:, -1]
       assert image.shape == (1, 2_56, 2_56, 3)
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  if gpta_config_file == "":
    __magic_name__								=			GPTaConfig()
  else:
    __magic_name__								=			GPTaConfig.from_json_file(A_   )
  __magic_name__								=			GPTaModel(A_   )

  # Load weights from numpy
  load_tf_weights_in_gpta(A_,	A_,	A_   )

  # Save pytorch-model
  __magic_name__								=			pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  __magic_name__								=			pytorch_dump_folder_path + """/""" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''   )
  torch.save(model.state_dict(),	A_   )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''   )
  with open(A_,	"""w""",	encoding="""utf-8"""   ) as f:
    f.write(config.to_json_string()   )


if __name__ == "__main__":
    __lowerCAmelCase     :	List[str]       			=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--gpt2_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained OpenAI model. \n'
            'This specifies the model architecture.'
        ),
    )
    __lowerCAmelCase     :	Tuple       			=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)

